High rates of obesity and obesity-related chronic disease persist among low-income children and adults in Baltimore City. The causes of this epidemic are multifactorial, and require multi-level and multi-component policies and programs. However, developing, testing, and evaluating interventions for obesity control can cost considerable time, effort, and resources. Computational simulation models offer an effective manner of determining the impact and unintended consequences of policies and interventions before implementation in the real world. Our team has expertise and experience in developing computational simulation models to guide policy makers and other stakeholders and in combining these with data collection and intervention studies, including a basic agent-based model (ABM) to represent children in the low income Baltimore City food environment. The overall objective of this proposal is to conduct mixed methods formative research to further develop the agent-based model of the low income Baltimore food environment, which will then be used to engage and work with policy makers, funders and other key stakeholders. The following specific aims will be addressed: 1. To conduct formative research to collect additional data on the food behaviors and environment. 2. To iteratively revise our existing ABM with this collected data and then utilize the progressively revised models to test the impact of and refine policies/program strategies for these venues. 3. To develop a process and the associated ABM tool that can be used to guide data collection, study development and implementation of obesity prevention programs and policies. 4. To offer our approach and a transferable ABM to the Nutrition and Obesity Policy Research and Evaluation Network (NOPREN) and others. The proposed work will have 5 phases: 1) Mixed methods formative research; 2) Revision of the Baltimore Low Income Food Environment Model (BLIFE) model/begin modifications for transferability; 3) Iterative data collection, model revision, interaction of researcher-practitionr teams; 4) Documentation of process and development of user-friendly version of the BLIFE model; and 5) Finalization of transferable version; support to other NOPREN Centers. The proposed work is highly innovative. The use of computational modeling to address obesity control has many gaps, including lack of attention to evaluating specific policies; little work usig geospatially specific information; limited efforts to model food source and food choice decisions of individual agents built on real data; and lack of transferability. BLIFE model will be the first geospatially specific computational model to include detailed data on most components of a city's low income food and physical activity environments, and will incorporate data from real adults and children. It will be the first use of agent-based modeling to support local policymakers as they formulate policies and programs to improve the urban food environment and reduce obesity risk in low income populations, and it will lead to the development of an ABM tool that can be disseminated to researchers and policy makers in other urban settings. We are submitting this application to become a NOPREN Collaborating Center (SIP 14-027). The aims and objective of our proposed Collaborating Center are directly in line with the HP 2020 goals related to obesity reduction, reductions in sugar and fat consumption, and increases in whole grain, and fruit and vegetable consumption. Our Center will contribute to the National Prevention Strategy by identifying policies and programs to improve access to healthy foods and beverages, and supports a CDC Winnable Battle with the same aim.